Analysis of HCRF-based modeling for a 1000-speakers identification task

• Main Authors: Chia-Hung Tseng, 曾家宏
• Other Authors: 洪維廷
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/68782005443768534631

碩士 === 元智大學 === 通訊工程學系 === 100 === In this thesis, we applied the Hidden Conditional Random Fields to a 1000-speakers identification task and compared the performance and computation cost of HCRF with the traditional Hidden Markov Models (HMMs). The experimental results indicate that HCRF models consume less training/testing time than the requirement of HMM; and furthermore HCRFs achieve a higher recognition rate than HMMs with the same system resources. In addition, we propose a constraint optimization method for training HCRF models. The proposed algorithm makes error rate of HCRF model lower than the method using the traditional Generalized Probabilistic Descent (GPD) method. Finally, we also discuss the performance of HCRF and HMM models in cross-group identification.